1. 1 SIMS 290-2: Applied Natural Language Processing Preslav Nakov October 6, 2004

2. 2 Today The 20 Newsgroups Text Collection WEKA: Exporer WEKA: Experimenter Python Interface to WEKA WEKA: Real-time Demo

3. 3 The 20 Newsgroups Text Collection WEKA: Exporer WEKA: Experimenter Python Interface to WEKA WEKA: Real-time Demo

4. 4 Source: originally collected by Ken Lang Content and structure: approximately 20,000 newsgroup documents –19,997 originally –18,828 without duplicates partitioned evenly across 20 different newsgroups Some categories are strongly related (and thus hard to discriminate): 20 Newsgroups Data Set http://people.csail.mit.edu/u/j/jrennie/public_html/20Newsgroups/ comp.graphics comp.os.ms-windows.misc comp.sys.ibm.pc.hardware comp.sys.mac.hardware comp.windows.x rec.autos rec.motorcycles rec.sport.baseball rec.sport.hockey sci.crypt sci.electronics sci.med sci.space misc.forsaletalk.politics.misc talk.politics.guns talk.politics.mideast talk.religion.misc alt.atheism soc.religion.christian computers

5. 5 Sample Posting: “talk.politics.guns” From: cdt@sw.stratus.com (C. D. Tavares) Subject: Re: Congress to review ATF's status In article, lvc@cbnews.cb.att.com (Larry Cipriani) writes: > WASHINGTON (UPI) -- As part of its investigation of the deadly > confrontation with a Texas cult, Congress will consider whether the > Bureau of Alcohol, Tobacco and Firearms should be moved from the > Treasury Department to the Justice Department, senators said Wednesday. > The idea will be considered because of the violent and fatal events > at the beginning and end of the agency's confrontation with the Branch > Davidian cult. Of course. When the catbox begines to smell, simply transfer its contents into the potted plant in the foyer. "Why Hillary! Your government smells so... FRESH!" -- cdt@rocket.sw.stratus.com --If you believe that I speak for my company, OR cdt@vos.stratus.com write today for my special Investors' Packet... reply from subject signature Need special handling during feature extraction… … writes:

6. 6 The 20 Newsgroups Text Collection WEKA: Exporer WEKA: Experimenter Python Interface to WEKA WEKA: Real-time Demo

7. 7 Slide adapted from Eibe Frank's WEKA: The Bird Copyright: Martin Kramer (mkramer@wxs.nl), University of Waikato, New Zealandmkramer@wxs.nl

8. 8 WEKA: Terminology Some synonyms/explanations for the terms used by WEKA, which may differ from what we adopted: Attribute: feature Relation: collection of examples Instance: collection in use Class: category

9. 9 Slide adapted from Eibe Frank's WEKA: The Software Toolkit Machine learning/data mining software in Java GNU License Used for research, education and applications Complements “Data Mining” by Witten & Frank Main features: data pre-processing tools learning algorithms evaluation methods graphical interface (incl. data visualization) environment for comparing learning algorithms http://www.cs.waikato.ac.nz/ml/weka

10. 10 Slide adapted from Eibe Frank's WEKA GUI Chooser java -Xmx1000M -jar weka.jar

11. 11 Slide adapted from Eibe Frank's Our Toy Example We demonstrate WEKA on a toy example: 3 categories from “20 Newsgroups”: –misc.forsale, –rec.sport.hockey, –comp.graphics 20 documents per category features: – words converted to lowercase – frequency 2 or more required – stopwords removed

12. 12 Slide adapted from Eibe Frank's Explorer: Pre-Processing The Data WEKA can import data is from: files: ARFF, CSV, C4.5, binary URL SQL database (using JDBC) Pre-processing tools (filters) are used for: Discretization, normalization, resampling, attribute selection, transforming and combining attributes, etc.

13. 13 List of attributes (last: class variable) Frequency and categories for the selected attribute Statistics about the values of the selected attribute Classification Filter selection Manual attribute selection Statistical attribute selection Preprocessing The Preprocessing Tab

14. 14 Slide adapted from Eibe Frank's Explorer: Building “Classifiers” Classifiers in WEKA are models for: classification (predict a nominal class) regression (predict a numerical quantity) Learning algorithms: Naïve Bayes, decision trees, kNN, support vector machines, multi-layer perceptron, logistic regression, etc. Meta-classifiers: cannot be used alone always combined with a learning algorithm examples: boosting, bagging etc.

15. 15 Choice of classifier The attribute whose value is to be predicted from the values of the remaining ones. Default is the last attribute. Here (in our toy example) it is named “class”. Cross-validation: split the data into e.g. 10 folds and 10 times train on 9 folds and test on the remaining one The Classification Tab

16. 16 Choosing a classifier

17. 17

18. 18 False: Gaussian True: kernels (better) displays synopsis and options numerical to nominal conversion by discretization outputs additional information

19. 19

20. 20

21. 21 all other numbers can be obtained from it different/easy class accuracy

22. 22 Contains information about the actual and the predicted classification All measures can be derived from it: accuracy: (a+d)/(a+b+c+d) recall: d/(c+d) => R precision: d/(b+d) => P F-measure: 2PR/(P+R) false positive (FP) rate: b/(a+b) true negative (TN) rate: a/(a+b) false negative (FN) rate: c/(c+d) These extend for more than 2 classes: see previous lecture slides for details Confusion matrix predicted –+ true –ab +cd

23. 23 Outputs the probability distribution for each example Predictions Output

24. 24 Probability distribution for a wrong example: predicted 1 instead of 3 Naïve Bayes makes incorrect conditional independence assumptions and typically is over-confident in its prediction regardless of whether it is correct or not. Predictions Output

25. 25 Error Visualization

26. 26 Error Visualization Little squares designate errors Axes show example number

27. 27 Slide adapted from Eibe Frank's Find which attributes are the most predictive ones Two parts: search method: –best-first, forward selection, random, exhaustive, genetic algorithm, ranking evaluation method: –information gain, chi-squared, etc. Very flexible: WEKA allows (almost) arbitrary combinations of these two Explorer: Attribute Selection

28. 28 Individual Features Ranking

29. 29 misc.forsale comp.graphics rec.sport.hockey Individual Features Ranking

30. 30 misc.forsale comp.graphics rec.sport.hockey ??? random number seed Individual Features Ranking

31. 31 Slide adapted from Jakulin, Bratko, Smrke, Demšar and Zupan's feature correlation 2-Way Interactions Feature Interactions C BA category feature importance of feature B importance of feature A

32. 32 Slide adapted from Jakulin, Bratko, Smrke, Demšar and Zupan's 3-Way Interaction: What is common to A, B and C together; and cannot be inferred from pairs of features. Feature Interactions C BA category feature importance of feature B importance of feature A

33. 33 Slide adapted from Guozhu Dong's Feature Subsets Selection Problem illustration Full set Empty set Enumeration Search Exhaustive/Complete (enumeration/branch&bounding) Heuristic (sequential forward/backward) Stochastic (generate/evaluate) Individual features or subsets generation/evaluation

34. 34 Features Subsets Selection

35. 35 misc.forsale comp.graphics rec.sport.hockey 17,309 subsets considered 21 attributes selected Features Subsets Selection

36. 36 Saving the Selected Features All we can do from this tab is to save the buffer in a text file. Not very useful... But we can also perform feature selection during the pre-processing step... (the following slides)

37. 37 Features Selection on Preprocessing

38. 38 Features Selection on Preprocessing

39. 39 Features Selection on Preprocessing 679 attributes: 678 + 1 (for the class)

40. 40 Features Selection on Preprocessing Just 22 attributes remain: 21 + 1 (for the class)

41. 41 Run Naïve Bayes With the 21 Features higher accuracy 21 Attributes

42. 42 different/easy class accuracy (AGAIN) Naïve Bayes With All Features ALL 679 Attributes (repeated slide)

43. 43 Sometimes WEKA has a weird naming for some algorithms Here is how to find the algorithms Barbara introduced: Naïve Bayes: weka.classifiers.bayes.NaiveBayes Perceptron: weka.classifiers.functions.VotedPerceptron Winnow: weka.classifiers.functions.winnow Decision tree: weka.classifiers.trees.J48 Support vector machines: weka.classifiers.functions.SMO k nearest neighbor: weka.classifiers.lazy.IBk Some of these are more sophisticated versions of the classic algorithms e.g. I cannot find the classic Naïve Bayes in WEKA (although there are 5 available implementations). Some Important Algorithms

44. 44 The 20 Newsgroups Text Collection WEKA: Explorer WEKA: Experimenter Python Interface to WEKA WEKA: Real-time Demo

45. 45 Slide adapted from Eibe Frank's Experimenter makes it easy to compare the performance of different learning schemes Problems: classification regression Results: written into file or database Evaluation options: cross-validation learning curve hold-out Can also iterate over different parameter settings Significance-testing built in! Performing Experiments

46. 46 Experiments Setup

47. 47 Experiments Setup

48. 48 Experiments Setup CSV file: can be open in Excel datasets algorithms

49. 49 Experiments Setup

50. 50 Experiments Setup

51. 51 Experiments Setup

52. 52 Experiments Setup

53. 53 Experiments Setup accuracy SVM is the best Decision tree is the worst SVM is statistically better than Naïve Bayes Decision tree is statistically worse than Naïve Bayes

54. 54 Experiments: Excel Results are output into an CSV file, which can be read in Excel!

55. 55 The 20 Newsgroups Text Collection WEKA: Explorer WEKA: Experimenter Python Interface to WEKA WEKA: Real-time Demo

56. 56 Slide adapted from Eibe Frank's @relation heart-disease-simplified @attribute age numeric @attribute sex { female, male} @attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina} @attribute cholesterol numeric @attribute exercise_induced_angina { no, yes} @attribute class { present, not_present} @data 63,male,typ_angina,233,no,not_present 67,male,asympt,286,yes,present 67,male,asympt,229,yes,present 38,female,non_anginal,?,no,not_present... WEKA File Format: ARFF Other attribute types: String Date Numerical attribute Nominal attribute Missing value

57. 57 Value 0 is not represented explicitly Same header (i.e @relation and @attribute tags) the @data section is different Instead of @data 0, X, 0, Y, "class A" 0, 0, W, 0, "class B" We have @data {1 X, 3 Y, 4 "class A"} {2 W, 4 "class B"} This is especially useful for textual data (why?) But! Problems with feature selection: cannot save results WEKA File Format: Sparse ARFF

58. 58 Python Interface to WEKA Works on the 20 newsgroups collection Extracts the features currently words easy to modify, just change one or more of: –extract_features_and_freqs() –is_feature_good() –build_stoplist() Allows to filter out: the stopwords the infrequent features Features are weighted by document frequency Produces an ARFF file to be used by WEKA

59. 59 Python Interface to WEKA Allows to specify: which subset of classes to consider the number of documents for each class the minimum feature frequency regular expression pattern a feature should match whether to remove the stopwords whether to convert words to lowercase kind of output to produce: sparse (i.e., feature = value) full vector (list of values)

60. 60 Python Interface to WEKA: How To Needs installed "20_newsgroups“ and "stopwords“ To get the things working under Windows: open “__init__.py” in the code below, substitute “/” with “\\” ################################################### ## 20 Newsgroups groups = [(ng, ng+'/.*') for ng in ''' alt.atheism rec.autos sci.space comp.graphics rec.motorcycles soc.religion.christian comp.os.ms-windows.misc rec.sport.baseball talk.politics.guns comp.sys.ibm.pc.hardware rec.sport.hockey talk.politics.mideast comp.sys.mac.hardware sci.crypt talk.politics.misc comp.windows.x sci.electronics talk.religion.misc misc.forsale sci.med'''.split()] twenty_newsgroups = SimpleCorpusReader( '20_newsgroups', '20_newsgroups/', '.*/.*', groups, description_file='../20_newsgroups.readme') del groups # delete temporary variable

61. 61 Python Interface to WEKA The Main Function

62. 62 Python Interface to WEKA Example Usage Python dictionary Estimated over the whole set! cross-validation: OK; test/train: not OK Use 1

63. 63 Python Interface to WEKA Functions You Will Probably Want To Modify convert to lowercase Also: stemming! Also: word+POS! Also: compounds!

64. 64 Python Interface to WEKA You might want to add… Stemming Porter stemmer >>> cats = Token(TEXT='cats', POS='NN') >>> from nltk.stemmer.porter import * >>> porter = PorterStemmer() >>> porter.stem(cats) >>> print cats WordNet stemmer morphy – morphological analyzer you need the following packages installed: –nltk.wordnet –nltk-contrib.pywordnet >>> from nltk_contrib.pywordnet.stemmer import * >>> morphy('dogs') 'dog'

65. 65 Python Interface to WEKA You might want to add… TF.IDF TF.IDF: t ij  log(N/n i ) TF –t ij : frequency of term i in document j –this is how features are currently weighted IDF: log(N/n i ) –n i : number of documents containing term i –N: total number of documents Modify the function extract_features_and_freqs_forall()

66. 66 The 20 Newsgroups Text Collection WEKA: Explorer WEKA: Experimenter Python Interface to WEKA WEKA: Real-time Demo

67. 67 Summary The 20 Newsgroups Text Collection WEKA: The Toolkit Explorer –Classification –Feature selection Experimenter ARFF file format Python Interface to WEKA feature extraction stemming Weighting: TF.IDF WEKA: Real-time Demo